EP 4279125 A1 20231122 - JOINT TRAINING OF DEEP NEURAL NETWORKS ACROSS CLINICAL DATASETS FOR AUTOMATIC CONTOURING IN RADIOTHERAPY APPLICATIONS
Title (en)
JOINT TRAINING OF DEEP NEURAL NETWORKS ACROSS CLINICAL DATASETS FOR AUTOMATIC CONTOURING IN RADIOTHERAPY APPLICATIONS
Title (de)
GEMEINSAMES TRAINING VON TIEFEN NEURONALEN NETZEN ÜBER KLINISCHE DATENSÄTZE ZUR AUTOMATISCHEN KONTURIERUNG IN STRAHLENTHERAPIEANWENDUNGEN
Title (fr)
APPRENTISSAGE CONJOINT DE RÉSEAUX NEURONAUX PROFONDS À TRAVERS DES ENSEMBLES DE DONNÉES CLINIQUES POUR LE CONTOURNAGE AUTOMATIQUE DANS DES APPLICATIONS DE RADIOTHÉRAPIE
Publication
Application
Priority
US 202263364995 P 20220519
Abstract (en)
Joint training techniques to train multiple models across clinical datasets for automatic contouring. Rather than using separate deep neural networks that are trained independently for each different dataset (e.g., a different image contrast or anatomy), joint training can be used to train multiple models simultaneously across clinical datasets for automatic contouring. By taking advantage of commonalities between two or more datasets, the techniques effectively take advantage of data that would otherwise be considered irrelevant to the task - allowing the user to train more performant models while requiring less training data per dataset.
IPC 8 full level
A61N 5/10 (2006.01)
CPC (source: EP US)
A61N 5/1031 (2013.01 - EP); G06N 3/045 (2023.01 - EP); G06N 3/09 (2023.01 - EP); G06N 20/00 (2018.12 - US); G06T 7/0012 (2013.01 - EP); G06T 7/11 (2016.12 - EP); G16H 20/40 (2017.12 - EP); G16H 30/20 (2017.12 - US); A61N 2005/1041 (2013.01 - EP); G06N 3/0464 (2023.01 - EP); G06T 2207/10116 (2013.01 - EP); G06T 2207/20081 (2013.01 - EP); G06T 2207/20084 (2013.01 - EP)
Citation (applicant)
US 63364995 P
Citation (search report)
- [XY] US 2021192279 A1 20210624 - LAAKSONEN HANNU MIKAEL [FI], et al
- [XY] US 2021065360 A1 20210304 - LAAKSONEN HANNU [FI], et al
- [A] US 2021304402 A1 20210930 - MORGAS TOMASZ [US], et al
- [Y] US 2017103287 A1 20170413 - HAN XIAO [US]
- [Y] US 2019370965 A1 20191205 - LAY NATHAN S [US], et al
Designated contracting state (EPC)
AL AT BE BG CH CY CZ DE DK EE ES FI FR GB GR HR HU IE IS IT LI LT LU LV MC ME MK MT NL NO PL PT RO RS SE SI SK SM TR
Designated extension state (EPC)
BA
Designated validation state (EPC)
KH MA MD TN
DOCDB simple family (publication)
DOCDB simple family (application)
EP 23171940 A 20230505; US 202318187122 A 20230321